code: def rayleigh(
      random_state,
      scale=1.0,
      size=None,
      chunk_size=None,
      gpu=None,
      dtype=None,
  ):
      r"""
      Draw samples from a Rayleigh distribution.
  
      The :math:`\chi` and Weibull distributions are generalizations of the
      Rayleigh.
  
      Parameters
      ----------
      scale : float or array_like of floats, optional
          Scale, also equals the mode. Should be >= 0. Default is 1.
      size : int or tuple of ints, optional
          Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
          ``m * n * k`` samples are drawn.  If size is ``None`` (default),
          a single value is returned if ``scale`` is a scalar.  Otherwise,
          ``mt.array(scale).size`` samples are drawn.
      chunk_size : int or tuple of int or tuple of ints, optional
          Desired chunk size on each dimension
      gpu : bool, optional
          Allocate the tensor on GPU if True, False as default
      dtype : data-type, optional
        Data-type of the returned tensor.
  
      Returns
      -------
      out : Tensor or scalar
          Drawn samples from the parameterized Rayleigh distribution.
  
      Notes
      -----
      The probability density function for the Rayleigh distribution is
  
      .. math:: P(x;scale) = \frac{x}{scale^2}e^{\frac{-x^2}{2 \cdotp scale^2}}
  
      The Rayleigh distribution would arise, for example, if the East
      and North components of the wind velocity had identical zero-mean
      Gaussian distributions.  Then the wind speed would have a Rayleigh
      distribution.
  
      References
      ----------
      .. [1] Brighton Webs Ltd., "Rayleigh Distribution,"
             http://www.brighton-webs.co.uk/distributions/rayleigh.asp
      .. [2] Wikipedia, "Rayleigh distribution"
             http://en.wikipedia.org/wiki/Rayleigh_distribution
  
      Examples
      --------
      Draw values from the distribution and plot the histogram
  
      >>> import matplotlib.pyplot as plt
      >>> import mars.tensor as mt
  
      >>> values = plt.hist(mt.random.rayleigh(3, 100000).execute(), bins=200, normed=True)
  
      Wave heights tend to follow a Rayleigh distribution. If the mean wave
      height is 1 meter, what fraction of waves are likely to be larger than 3
      meters?
  
      >>> meanvalue = 1
      >>> modevalue = mt.sqrt(2 / mt.pi) * meanvalue
      >>> s = mt.random.rayleigh(modevalue, 1000000)
  
      The percentage of waves larger than 3 meters is:
  
      >>> (100.*mt.sum(s>3)/1000000.).execute()
      0.087300000000000003
      """
      if dtype is None:
          dtype = (
              np.random.RandomState()
              .rayleigh(handle_array(scale), size=(0,))
              .dtype
          )
      size = random_state._handle_size(size)
      seed = gen_random_seeds(1, random_state.to_numpy())[0]
      op = TensorRayleigh(size=size, seed=seed, gpu=gpu, dtype=dtype)
      return op(scale, chunk_size=chunk_size)
  
file_path: mars/mars/tensor/random/rayleigh.py
imports: import numpy
  from ...None import opcodes
  from ...serialization.serializables import AnyField
  from ..utils import gen_random_seeds
  from .core import TensorRandomOperandMixin, handle_array, TensorDistribution
label_desc: Use image node_id 1 for calling a global function with example usage: rayleigh(random_state, scale, size, chunk_size, gpu, dtype) and returns: op